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Record W1996148540 · doi:10.1109/tie.2013.2267706

Three-Phase Multimodule VSIs Using SHE-PWM to Reduce Zero-Sequence Circulating Current

2013· article· en· W1996148540 on OpenAlexaff
Mehdi Narimani, Gerry Moschopoulos

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2013
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsWestern University
Fundersnot available
KeywordsPulse-width modulationHarmonicsInverterModular designThree-phaseVoltageComputer scienceElectronic engineeringVoltage sourceControl theory (sociology)Power (physics)EngineeringElectrical engineeringPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

A modular voltage-source inverter (MVSI) uses individual three-phase modules in a shunt connection to allow lower power rated inverters to be implemented in higher power applications. It is standard practice to implement each module with the same pulsewidth-modulation (PWM) pattern. This paper proposes a new PWM approach for MVSIs for which each module is made to operate with a different PWM pattern. It is shown in this paper how more output voltage harmonics can be eliminated with the new approach than with the standard approach and how the new approach can be expanded to significantly reduce zero-sequence current that circulates among the inverter modules. The mathematical formulations needed to generate appropriate PWM patterns for the new PWM approach and experimental results obtained from a prototype MVSI are presented in this paper as well.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.094
GPT teacher head0.303
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations76
Published2013
Admission routes1
Has abstractyes

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